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Does any evidence show that Smartphone users have poorer memory?

Does any evidence show that Smartphone users have poorer memory?



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An ages old complaint is that new technology harms memory. Why remember something when you can look it up?

In a course on Human Memory I distinctly recall an interesting discussion on phones and memory, and an interesting point came up; even though smart phones mean we no longer remember phone numbers as often as we used to we have to remember passwords, userids and other trivial bits of information which keeps our encoding skills sharp. However I don't recall and studies to support this hypothesis.

Are there any recent studies to suggest whether users of portable digital tools like smartphones actually perform worse on any memory-related psychometrics?

And no I'm not talking about these extremely questionable Cellphones Harm Memory in Mice stories the media has picked up on. I'm not concerned with electromagnetic radiation's effects on the brain, but rather the effect of having easy access to external information.


One relevant study by Sparrow et al. (2011) that came out last year in Science was on the "Google effect": When subjects expected that they'd be able to have later access to information, their memory was poorer for it. We can extrapolate to smartphones -- if individuals know they have information at their fingertips, they don't need to worry so much about retaining that information.

Betsy Sparrow, Jenny Liu, and Daniel M. Wegner (2011) Google Effects on Memory: Cognitive Consequences of Having Information at Our Fingertips Science 1207745 Published online 14 July 2011 [DOI:10.1126/science.1207745] (Free PDF)


As far as I know, there doesn't seem to be any straight-forward answer to this question.

The research mentioned in Andy's answer does affirm that there is a loss in memory but there is a difference between searching Google and looking a phone number on smartphone. In case of Google search, you most probably select a link at the fourth or fifth variation of your search. But in case of smartphones, you are just going to have a look at the address book to get your phone-number.

Keeping in view the wider context of the question, cognition is most probably regarded as an action happening within the mind. Simply put it, we reach a decision by using our cognitive skills alone without any reference to any external source at the time of decision. This is not the case.

If an external source is always going to aid our process of cognition, it could well be constructed a part of our cognition provided we can always have access to it and could replicate the cognitive process in an exact manner. From your question, the address book in a smartphone can be considered part of our wider memory provided we can always have access to it and hence can be regarded as aiding in our cognition.

Since it is difficult to draw a line between the internal and external cognition processes, generally checking an external source for information seem to imply a loss of memory. It must be noted that memory isn't lost only the memory about the phone numbers is lost since you have created a reliable external cognition source that could look phone numbers for you.

The seminal article, The Extended Mind, by Clark (1998), gives a much better explanation and would open up other avenues for thinking within this context.

Clark, Andy & Chalmers, David J. (1998). The extended mind. Analysis 58 (1):7-19. (HTML)


Understanding Memory in Advertising

Advertisers and those who measure the impact of advertising are obsessed with memory. If advertising is to be successful, it has to stick in the consumer’s memory—or so the saying goes. But what exactly is that thing called memory, how long does it linger, and how do we measure it?

At the first level, memory can be divided into two types: Explicit memory, which refers to information we are aware of (the facts and events we can consciously access), and implicit memory, which refers to information we’re not consciously aware of (it’s stored in our brain and can affect behavior, but we can’t recall it). Explicit memory can be further divided into episodic memory and semantic memory. Episodic memory is the memory of an event in space and time—it includes other contextual information present at that time. On the other hand, semantic memory is a more structured record of facts, meanings, concepts and knowledge that is divorced from accompanying episodic details.

How do these various types of memories come in play in advertising? Advertising memories that we retrieve through standard recall and recognition cues are episodic. Here are a few questions that researchers might use to retrieve those memories: What brand of smartphone did you see advertised on TV last night? Do you recall if it was a Samsung Galaxy S7 or an iPhone 7? What if I told you it aired during last night’s episode of Madam Secretary? What if I told you it featured a father shooting a video of his young daughter playing a scene in Romeo and Juliet? But very often, consumers cannot tell us exactly how they came to know what they know about a brand. They know that Coca-Cola is refreshing, for instance, but cannot tell us exactly how they first came by that information. Was it an ad they saw, a word from a friend, a personal experience? That memory is semantic. Unconscious associations (such as a non-accessible childhood experience of drinking Coca-Cola during a hot summer) create implicit memories that can continue to affect brand preferences much later in life.

Memory is a complex concept, with different types of memories serving different roles, and the nature and content of our memories changes over time. If consumers can’t remember what they saw last night without a prompt, but something they saw years ago still has an effect on them, it’s important that we, as researchers, gain a better understanding of the impact that time has on memory.

Research tells us that memories start to decay immediately after they’re formed. That decay follows a curve that is very steep at the beginning (the steepest rate of decay occurs in the first 24 hours) and levels off over time. In a controlled experiment, Nielsen tested the memorability of 49 video ads immediately after consumers were exposed to them in a clutter reel, and we tested that memorability again the day after exposure (among a separate group of people). Levels of branded recognition had fallen nearly in half overnight. This isn’t just happening in the lab: Nielsen’s in-market tracking data shows similar patterns.

Does this rapid decay of memory spell doom for the ad industry? Not at all. The fact that a specific memory can’t be recalled doesn’t mean that it’s fully gone. For one, relearning explicit information that is almost fully forgotten is much faster than learning it the first time around. Practice (repetition) indeed makes perfect—and can help create durable memories. In addition, the most striking revelation of a decay curve is not the steep decline at the start, but rather the leveling-off that occurs over the long term. We studied brand memorability decay over a longer period of time for a number of digital video ads recently, and while recall dropped for all ads by 50% in the first 24 hours (as was the case in our earlier study), it still stood at that same 50% level five days later for half of the brands.

What does this tell us about measuring memory? First, that time between exposure and measurement matters. The 24 hours mark is ideal because that’s the point where the memory curve starts to flatten. Second, that advertising memories are encoded in context (asking questions about the show in which the ad aired, for instance, is going to help consumers remember that ad). Finally, that memories can endure—either via repetition for explicit types of memories, or via implicit internalization.

To help advertisers in today’s cluttered advertising environment, researchers need to measure memory in all its forms. At Nielsen, we capture important performance metrics for ad memorability using carefully crafted surveys, and those surveys are conducted in a way that produces reliable benchmarks for the industry. And with the tools of neuroscience*, we can now measure brain activity during exposure and monitor both explicit and implicit memory systems with second-by-second granularity. Together, these different research techniques are helping us better understand the nature of memories—and how memories and advertising come to interact.

*See From theory to common practice: consumer neuroscience goes mainstream in VOL 1 ISSUE 2 of the Nielsen Journal of Measurement.


The health effects of screen time on children: A research roundup

This research roundup looks at the effects of screen time on children’s health.

This research roundup, originally published in May 2019, has been updated to include a recent systematic review and meta-analysis looking at the effects of screen time on academic performance.

Gone are visions of idyllic childhoods spent frolicking in fields and playing in pastures for many kids, green grass has been replaced with smartphone screens.

This is in spite of official guidelines from the American Academy of Pediatrics, which recommends less than one hour per day of screen time for children between the ages of 2 and 5, and, for older children, “consistent limits” on screen time and prioritization of sleep, physical activity and other healthy behaviors over media use. Just last month the World Health Organization issued guidelines on the subject, stressing that children between the ages of 2 and 4 should have no more than one hour of screen time per day.

The ubiquity of screens and their prominence in everyday life has drawn criticism and concerns, with Microsoft veteran and philanthropist Melinda Gates writing about not being “prepared for smartphones and social media” as a parent and news headlines questioning whether smartphones have “destroyed a generation.”

But what does the research say? This roundup looks at the effects of screen time on children’s health. Studies range from childhood to adolescence and focus on topics including sleep, developmental progress, depression and successful interventions to reduce screen time.

This study analyzes parent-reported data about screen time and behavioral issues such as inattention and aggressiveness for a sample of 2,322 Canadian preschool-age children. Researchers found that over 13% of kids in the sample were exposed to over two hours of screen time each day including watching TV and DVDs, playing video games or using a computer, tablet or mobile device. The effects: Kids who were exposed to more screen time “showed significantly increased behavior problems at five-years,” the authors write. “Briefly, children who watched more than 2 hours of screen time/day had increased externalizing [e.g., attention and behavior], internalizing [e.g., anxiety and depression], and total behavior problems scores compared to children who watched less than 20 minutes.” Attention problems in particular were apparent in children who had over two hours of screen time each day.

Toddlers who use mobile devices daily are more likely to experience speech delays, according to an analysis of parent-reported data on 893 children in the greater Toronto area of Canada. While 78% of parents said their kids spent no time on mobile devices, the other 22% reported a range of 1.4 to 300 minutes daily, with a median of 15.7 minutes.

In total, 6.6% of parents reported expressive speech delays (i.e., late to begin talking). The prevalence of other communication delays, such as lack of use of gestures and eye gaze, was 8.8%. The researchers found a positive association between mobile device use and expressive speech delays. “An increase in 30 minutes per day in mobile media device use was associated with a 2.3 times increased risk of parent-reported expressive speech delay,” the authors write. Other communication delays were not linked to device use. The researchers suggest the connection between device use and expressive speech delays might be explained by the fact that past research has shown infants “have difficulty applying what they learn across different contexts.” An alternate explanation is that these children who spend more time with devices might have less exposure to speech from caregivers.

Is screen time detrimental to child development? This study looks at data collected from 2,441 mothers and children in Canada at three different time points – when the children were 2, 3 and 5 years old. The researchers were interested in the total number of hours the children spent looking at screens each week as well as their progress in various developmental areas such as fine motor skills, communication and problem solving. The average amount of screen time for the age groups in the study: 17, 25 and 11 hours of television per week for 2-, 3-, and 5-year olds, respectively.

The researchers found that kids who spent more time watching screens at ages 2 and 3 did worse on developmental tests at the subsequent time points of 3 and 5 years. “To our knowledge, the present study is the first to provide evidence of a directional association between screen time and poor performance on development screening tests among very young children,” the authors write.

The researchers suggest that excessive screen time leads to developmental delays, rather than the other way around – negating the notion that children with developmental delays might receive more screen time to manage their behavior.

The three phase data capture supports this explanation because children with greater screen time at one time point go on at the next time point to have poorer developmental progress, but children with poor developmental performance at an earlier time point do not receive increased screen time at later time points.

This publication consists of both a systematic review and meta-analysis of research on the relationship between screen time and academic performance. The authors identified 58 studies to include in the systematic review, which provides a summary of the qualitative effects of screen time 30 of these studies were included in the subsequent meta-analysis, which the authors used to calculate the effect size of screen time on academic performance.

The 58 studies in the systematic review included 480,479 participants ranging from four to 18 years of age. The articles were published between 1958 and 2018 and represent the efforts of researchers around the world. The studies looked at computer, internet, mobile phone, television and video game use individually, as well as overall screen time. Outcomes of interest included school grades, performance on academic achievement tests, academic failure data, or self-reported academic achievement or school performance.

The key finding from the systematic review was that in most of the papers reviewed, as time spent watching television increased, academic performance suffered. Relationships were less clear-cut for other types of screen use.

The meta-analysis, which focused on a subset of 106,653 participants from the larger sample, did not find an association between overall screen time and academic performance. When the authors analyzed the data by type of activity, they found television watching was linked to poorer overall academic performance as well as poorer language and mathematics performance, separately. Time spent playing video games was negatively linked with composite academic performance scores, too. Analyzing the data further by age, the authors found that time spent with screens had a larger negative association with academic performance for adolescents than children.

“The findings from this systematic review and meta-analysis suggest that each screen-based activity should be analyzed individually because of its specific association with academic performance,” the authors conclude. “This study highlights the need for further research into the association of internet, computer, and mobile phone use with academic performance in children and adolescents. These associations seem to be complex and may be moderated and/or mediated by potential factors, such as purpose, content, and context of screen media use.”

The authors suggest that educators and health professionals should focus screen time reduction efforts on television and video games for their negative connections to academic performance and potential health risks due to their sedentary nature.

Screen Time Is Associated with Adiposity and Insulin Resistance in Children
Nightingale, Claire M. et al. Archives of Disease in Childhood, July 2017.

This study looks at the relationship between screen time and Type 2 diabetes risk factors, like being severely overweight, among 4,495 schoolchildren in the United Kingdom between the ages of 9 and 10. The short of it: Kids who spent over three hours daily on screen time were less lean and more likely to show signs of insulin resistance, which can contribute to the development of Type 2 diabetes, compared with their peers who reported one hour or less of screen time each day. Black children were more likely to spend over three hours daily on devices compared with their white and south Asian peers – 23% of black children fell into that group, compared with 16% of white children and 16% of south Asian children.

Digital Media and Sleep in Childhood and Adolescence
LeBourgeois, Monique K. et al. Pediatrics, November 2017.

This report summarizes 67 studies looking at associations between screen time and sleep health – adequate sleep length and quality — in children and adolescents. The main takeaways: A majority (90%) of the studies included in a systematic review of research on screen time in children and teenagers found adverse associations between screen time and sleep health – primarily because of later bedtimes and less time spent sleeping. Delving deeper, underlying mechanisms include “time displacement” (think scrolling Instagram for an hour that might otherwise be spent sleeping), psychological stimulation from content consumed and impacts of screen light on sleep patterns. The upshot? These kids are tired. The previously cited research review also indicates that a majority of studies saw a relationship between tiredness and screen time.

This study analyzes data from the 2011, 2013, 2015 and 2017 cycles of a nationally-administered, school-based survey on various health-related behaviors related to the leading causes of death and disability in the U.S. The researchers were interested in whether respondents met the recommendations for time spent on sleep, physical activity and screen time in a given day. A total of 59,397 adolescents were included in the data set.

The findings indicate that only 5% of adolescents surveyed met all three guidelines – that is, getting the recommended amount of sleep and physical activity and limiting screen time to less than two hours per day. There were disparities among the sample in terms of the odds of meeting all of the recommendations: 16- and 17-year-olds were less likely than those aged 14 and younger to meet all the guidelines black, Hispanic/Latino and Asian participants were less likely to meet the three guidelines than white participants overweight and obese participants were less likely to meet the guidelines than normal weight participants participants who reported marijuana use were less likely to meet the guidelines than those who did not. Participants who reported depressive symptoms were also less likely to meet all the guidelines.

This study looks at the same three outcomes examined above, but adds another component – “global cognition.” This is an overall cognition score assessed by the National Institutes of Health Toolbox – an iPad-based neuro-behavioral screening tool. The assessment measures various cognitive functions including memory, attention, vocabulary and processing speed. The sample included 4,520 participants between the ages of 8 and 11. Only 5% of participants met all three recommendations – and they were the better for it. “Compared with meeting none of the recommendations, associations with superior global cognition were found in participants who met all three recommendations, the screen time recommendation only, and both the screen time and the sleep recommendations,” the authors write.

This study looks at the relationship between screen time and depression and suicide rates in 506,820 adolescents in the U.S. between 2010 and 2015. The data on screen time use and mental health issues came from two nationally representative surveys of students in grades 8 through 12. Suicide rates were calculated from national statistics collected by the Centers for Disease Control and Prevention’s Fatal Injury Reports.

The analysis finds a “clear pattern linking screen activities with higher levels of depressive symptoms/suicide-related outcomes [suicidal ideation — that is, thinking about suicide — and attempts] and nonscreen activities with lower levels.” Among participants who used devices for over five hours each day, nearly half – 48% — reported at least one suicide-related outcome. In comparison, 29% of those who used devices for just an hour per day had at least one suicide-related outcome.

Overall, during the time studied, suicide rates, depressive symptoms and suicide-related outcomes increased. Girls accounted for most of the rise – they were more likely to experience depressive symptoms and suicide-related outcomes than boys they also experienced stronger effects of screen time on mental health. In particular, girls, but not boys, had a significant correlation between social media use and depressive symptoms.

This review looks at 10 systematic reviews and meta-analyses of research on interventions to reduce sedentary behaviors such as screen time among children and adolescents. The authors found that all of the included reviews determined “some level of effectiveness in reducing time spent in sedentary behavior.” Effects, however, were small. Interventions tended to be more successful among children younger than 6 years old. Strategies that were effective included restricting access to television through TV monitors, systems that use TV as a reward for physical activity and behavioral interventions such as setting goals and developing schedules for screen time.

For more research on the effects of screen time, check out our write ups of research that shows how smartphones make people unhappy and how they’re distracting even when they aren’t in use.


What your choice of smartphone says about you

Choice of smartphone provides valuable information about its owner.

This is one of the findings of a doctoral study conducted by Heather Shaw, from University of Lincoln's School of Psychology. She is presenting her work today, Thursday 1 September, to the British Psychological Society Social Psychology Section annual conference in Cardiff.

Miss Shaw and her fellow researchers conducted two studies of personality differences between iPhone and Android smartphone users. Lancaster University was also involved in the study.

In the first study the researchers asked 240 participants to complete a questionnaire about characteristics they associate with users of each smartphone brand.

In the second study they tested these stereotypes against actual personality traits of 530 Android and iPhone smartphone users.

The results from the first study showed that Android users are perceived to have greater levels of honesty and humility, agreeableness and openness personality traits but are seen as less extroverted than iPhone users.

The results from the second study showed that most of the personality stereotypes did not occur in reality, as only honesty and humility was found in greater amounts within Android users.

However, they did find that women were twice more likely to own an iPhone than an Android Phone. When measuring the characteristic 'avoidance of similarity' which describes whether people like having the same products as others, Android Users avoided similarity more than iPhone users. Finally, iPhone users thought it was more important to have a high status phone than Android users.

Heather explained "This study provides new insights into personality differences between different types of smartphone users. Smartphone choice is the most basic level of smartphone personalisation, and even this can tell us a lot about the user."

"Imagine if we further researched how personality traits relate to the applications people download. It is becoming more and more apparent that smartphones are becoming a mini digital version of the user, and many of us don't like it when other people use our phones because it can reveal so much about us."


Media Multitasking Disrupts Memory, Even in Young Adults

The bulky, modern human brain evolved hundreds of thousands of years ago and, for the most part, has remained largely unchanged. That is, it is innately tuned to analog information&mdashto focus on the hunt at hand or perhaps the forage for wild plants. Yet we now pummel our ancient thinking organ with a daily deluge of digital information that many scientists believe may have enduring and worrisome effects.

A new study published today in Nature supports the concern. The research suggests that &ldquomedia multitasking&rdquo&mdashor engaging with multiple forms of digital or screen-based media simultaneously, whether they are television, texting or Instagram&mdashmay impair attention in young adults, worsening their ability to later recall specific situations or experiences.

The authors of the new paper used electroencephalography&mdasha technique that measures brain activity&mdashand eye tracking to assess attention in 80 young adults between the ages of 18 and 26. The study participants were first presented with images of objects on a computer screen and asked to classify the pleasantness or size of each one. After a 10-minute break, the subjects were then shown additional objects and asked whether they were already classified or new. By analyzing these individuals&rsquo brain and eye responses as they were tasked with remembering, the researchers could identify the number of lapses in their attention. These findings were then compared to the results of a questionnaire the participants were asked to fill out that quantified everyday attention, mind wandering and media multitasking.

Higher reported media multitasking correlated with a tendency toward attentional lapses and decreased pupil diameter, a known marker of reduced attention. And attention gaps just prior to remembering were linked with forgetting the earlier images and reduced brain-signal patterns known to be associated with episodic memory&mdashthe recall of particular events.

Previous work had shown a connection between media multitasking and poorer episodic memory. The new findings offer clues as to why this might be the case. &ldquoWe found evidence that one&rsquos ability to sustain attention helps to explain the relationship between heavier media multitasking and worse memory,&rdquo says the paper&rsquos lead author Kevin Madore, a postdoctoral fellow in the department of psychology at Stanford University. &ldquoIndividuals who are heavier media multitaskers may also show worse memory because they have lower sustained attention ability.&rdquo

&ldquoThis is an impressive study,&rdquo comments Daphne Bavelier, a professor of psychology at the University of Geneva in Switzerland, who was not involved in the new research. &ldquoThe work is important as it identifies a source of interindividual variability when one is cued to remember information&rdquo&mdashthe differences in attention among the study participants. &ldquoThese findings are novel and tell us something important about the relationship between attention and memory, and their link to everyday behavior . [something] we did not know before,&rdquo adds Harvard University psychologist Daniel L. Schacter, who was also not involved in the study.

Madore points out that the new findings are, for now, correlational. They do not indicate if media multitasking leads to impaired attention or if people with worse attention and memory are just more prone to digital distractions. They also do not necessarily implicate any specific media source as detrimental to the brain. As work by Bavelier found, action video games in particular harbor plenty of potential for improving brain function.

But Madore and his colleagues, including senior author of the paper and Stanford psychologist Anthony D. Wagner, hope to clarify these unknowns in future studies. They also hope to pursue attention-training interventions that could help improve attention and memory in people prone to distraction.

With winter looming and the COVID-19 pandemic keeping us indoors, Madore feels the new study stresses the need to be mindful of how we engage with media. &ldquoI think our data point to the importance of being consciously aware of attentiveness,&rdquo he says, whether that awareness means resisting media multitasking during school lectures or work Zoom sessions or making sure not to idly flip through your Facebook feed while half watching the new Borat movie.

ABOUT THE AUTHOR(S)

Bret Stetka is a writer based in New York City and editorial director of Medscape Neurology (a subsidiary of WebMD). His work has appeared in Wired, NPR and the Atlantic. He graduated from the University of Virginia School of Medicine in 2005.


(Dis)Connected

Apple introduced its iPhone in 2007 and the world has never been the same. Though the iPhone wasn't the first internet-enabled "smartphone," its touchscreen technology and built-in app library made it the first to gain mass-market appeal—and it sparked a revolution. Now, wireless mobile devices have found their way into millions of pockets, functioning not just as phones but as internet browsers, messaging services, calendars, cameras, alarm clocks, road maps and video players.

By 2015, 72 percent of U.S. adults reported owning a smartphone, according to a study by the Pew Research Center (Pew, 2016). Smartphones undoubtedly make our lives easier, says Elizabeth Dunn, PhD, a psychology professor at the University of British Columbia, who studies the ways that mobile technology can support or undermine well-being. "Having the entire store of human knowledge at our fingertips is pretty useful," she quips. But there may be trade-offs for that convenience. Mobile technology also has the power to negatively influence our health andhappiness, she says. "Our lab has gone looking for pros, but in general we keep finding cons."

At their worst, research finds smartphones can mess with our sleep, stress us out and monopolize our attention. But psychology may hold the key to helping people take control of this technology to prevent such negative effects and even enhance our well-being. Tech developers aren't in the business of promoting well-being, Dunn says, but that does fall under the purview of psychology. "I think we need to devote a lot more attention to this," she says.


Conclusions

Our review indicates that approximately 1 in 4 CYP are demonstrating problematic smartphone use, a pattern of behaviour that mirrors that of a behavioural addiction. A consistent relationship has been demonstrated between PSU and deleterious mental health symptoms including: depression anxiety high levels of perceived stress and poor sleep. Younger populations are more vulnerable to psychopathological developments, and harmful behaviours and mental health conditions established in childhood can shape the subsequent life course. Further work is urgently needed to develop assessment tools for PSU, and prevent possible long-term widespread harmful impact on this and future generations’ mental health and wellbeing.


How to Break a Cell Phone Addiction

Whether you meet the criteria for a full-blown phone addiction or simply want to reduce your emotional dependency on technology, there are lots of helpful strategies for breaking this bad habit.

Outsmart your smartphone by using technology to limit your technology use. Want to use your phone less? There’s an app for that. In fact, there are LOTS of apps for that. The BreakFree app, for instance monitors your phone usage, tallying up the number of times your unlock the screen, how many minutes hours you spend on your phone, which apps you use the most. The app then gives you a daily addiction score. If your addiction score alone isn’t motivation enough to make you think twice before using your phone, the app also allows you to set up notifications to alert you when you’ve been on your phone for an extended period of time or opened an app too many times.

Get your phone out of the bedroom. There are lots of reasons why you should not sleep with your phone. For starters, using your phone within an hour of bedtime leads to poorer sleep quality and more insomnia. If you’re like me and you check your phone every time you wake up in the night, your sleep is even more negatively impacted. Furthermore, when you wake up and check your phone before getting out of bed, you are reinforcing the habit for the rest of the day. Buy a cheap alarm clock and stop sleeping with your phone by your side.

Put yourself on a digital diet. The same way reducing your waistline involves breaking unhealthy habits and eating more mindfully, reducing your screen time requires similar self-control. When you want to lose weight, you have to stop eating the junk food. When you want to cut back on smartphone use, you have to stop using the junk apps. Delete those deliciously addictive games. Cut back on social networks the way a nutritionist might suggest you cut back on carbs. Quitting technology cold turkey isn’t a realistic option for most people, so this requires some real will-power. Temporarily (if not permanently) deleting your most frequently used apps can be a huge help.

Set up a digital schedule. Assign certain chunks of time throughout the day to go phone free. Experiment with leaving your phone at home when you go to dinner with your friends. Turn your phone off for a couple hours every day at the office so you can work without distraction. Leave your phone in the other room in the evenings in order to spend more quality time with your partner or children.

Get drastic with a digital detox. If you are open to trying something more extreme, Daniel Sieberg, author of The Digital Diet: The 4-Step Plan to Break Your Tech Addiction and Regain Balance in Your Life, suggests doing a full “digital detox,” where you spend an entire weekend with ZERO access to technology. Notify your loved ones in advance, power your devices off and stick them in a box or a bottom drawer, and ask a trusted friend to temporarily change your passwords to reduce temptation. After the detox, Sieberg suggests reintroducing technology slowly. He swears that a digital diet does wonders for reconnecting with the real world and improving relationships.


Media Use Is Linked to Lower Psychological Well-Being: Evidence from Three Datasets

Adolescents spend a substantial and increasing amount of time using digital media (smartphones, computers, social media, gaming, Internet), but existing studies do not agree on whether time spent on digital media is associated with lower psychological well-being (including happiness, general well-being, and indicators of low well-being such as depression, suicidal ideation, and suicide attempts). Across three large surveys of adolescents in two countries (n = 221,096), light users (<1 h a day) of digital media reported substantially higher psychological well-being than heavy users (5+ hours a day). Datasets initially presented as supporting opposite conclusions produced similar effect sizes when analyzed using the same strategy. Heavy users (vs. light) of digital media were 48% to 171% more likely to be unhappy, to be in low in well-being, or to have suicide risk factors such as depression, suicidal ideation, or past suicide attempts. Heavy users (vs. light) were twice as likely to report having attempted suicide. Light users (rather than non- or moderate users) were highest in well-being, and for most digital media use the largest drop in well-being occurred between moderate use and heavy use. The limitations of using percent variance explained as a gauge of practical impact are discussed.

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Introduction

According to the 2011 Monitoring the Future Study, marijuana remains the most commonly used illicit drug in adolescence in the United States, one of few increasing in prevalence. In fact, marijuana has been the most commonly used illicit substance for almost 40 years, and presently 23% of 12 th graders in the U.S. report using marijuana in the past month [1]. Marijuana use in adolescence could have implications for academic functioning, as well as social and occupational functioning extending into later life. Maturational brain changes, particularly myelination and synaptic pruning, are occurring throughout adolescence, well into early adulthood [2]. These remodeling processes are purportedly linked to efficient neural processing, and believed to underlie specialized cognitive processing necessary for optimal neurocognitive performance.

Cannabinoid receptors (CB1) are widely distributed throughout the brain (e.g., hippocampus, prefrontal cortex), and play a role in neurotransmitter release and concentrations across neural systems (excitatory and inhibitory). It has been suggested that these receptors increase during adolescence, have a role in genetic expression of neural development, and that alteration of the endocannabinoid system during adolescence may results in a cascade of neurochemical and neurostructural aberrations, thus leading to poorer cognitive and emotional outcomes in adulthood [3, 4].

Disruptions in brain development related to neurotoxic effects of regular marijuana use could significantly alter neurodevelopmental trajectories by not only changing neurochemical communication and genetic expression of neural development, but causing a toxic effect on brain tissue. Such a marijuana-related effect on white matter and gray matter structures (e.g., changes in myelin, axons, and synapses) could have widespread implications for healthy brain development from childhood to young adulthood on subtle cognitive functioning and success in daily functioning. Studies exploring the neurocognitive consequences and structural and functional neuroimaging changes related to marijuana use in adolescence will be discussed, along with recommendations for future work.

Cognition

Adult studies of marijuana use often find subtle decreases in performance compared to controls in cognitive domains such as attention, memory, and processing speed such effects have been discussed as transient in the literature given limited group differences after prolonged abstinence from marijuana [5, 6]. It is unclear if findings translate to adolescent populations. Ongoing cognitive development in the domains of memory and executive functioning, and particularly in specialized functions like cognitive control, is not only tightly associated with adolescence and neocortical tissue maturation, but is likely to have implications for school performance and engagement in risk/reward behaviors [7]. One of the earliest studies on the effects of marijuana on adolescent neurocognitive development evaluated verbal and nonverbal memory performance in cannabis-dependent adolescents (ages 14 to 16) compared to matched controls [8]. Schwartz and colleagues found that short-term memory impairment persisted after six weeks of monitored abstinence. In contrast, Teichner and colleagues (2000) found no relationship between marijuana use severity and cognitive performance among cognitively impaired and unimpaired adolescents referred for drug treatment [9]. There have been considerable additions to the literature over the last decade, yet the degree of impairment related to marijuana use in adolescence remains inconclusive. A pattern of subtle yet potentially detrimental effects in cognitive domains related to attention, learning, and memory are most often described.

A prospective study conducted in 2005 examined neurocognitive performance among 17� year olds with history of soft drug exposure in utero compared to prior performance at 9� years old. Current heavy cannabis users performed significantly worse on measures of processing speed and memory, controlling for pre-drug performance. Notably, former heavy users (reporting 3 months without regular use) had similar scores to non-marijuana using controls [10]. In regard to higher-order cognitive functioning, Lane and colleagues (2007) found adolescents (ages 14�) with histories of heavy marijuana use performed worse on perseverative responding and flexible thinking compared to controls with limited histories of use. This same research group also found evidence of reduced motivation among marijuana users compared to controls [11, 12]. In 2007, Harvey and colleagues found adolescent marijuana users (age 13� use greater than once per week) performed worse on tests of attention, learning, and memory furthermore, poorer performance on executive functioning in this sample was related to more days of cannabis use in the past month [13].

Studies from our laboratory have largely found differences in similar domains following four weeks of monitored abstinence. Our first prospective investigation found that cumulative marijuana use over the course of eight years (teens followed from ages 13�) was related to poorer performance on measures of attentional functioning [14]. In a subsequent cross-sectional study of adolescent marijuana users ages 16�, we found that marijuana users demonstrated slower processing speed, poorer verbal learning and memory, and sequencing abilities [15]. In order to better understand acute changes with abstinence, we examined neurocognitive performance over 3 weeks of monitored abstinence in marijuana users ages 15�. Between-group differences in attention, learning, and memory were identified at baseline, however while learning and memory performance reached similar levels of performance to controls after 3 weeks of abstinence, attention differences persisted [16].

Group differences in our studies generally persist despite controlling for alcohol use present in both controls and marijuana users but to further understand differential contributions of marijuana and alcohol to neurocognitive functioning in our sample, we examined unique associations between alcohol use severity and cognitive functioning in both marijuana users as well as controls. In a recent investigation, we found that more self-reported alcohol withdrawal symptoms predicted poorer performance on learning and memory in a sample of non-marijuana using teens with histories of episodic alcohol use, despite no relationship in our marijuana users with similar and/or heavier self-reported history of alcohol use [17]. This suggests differential relationships between marijuana, alcohol, and cognitive outcomes in our sample. We have observed similar relationships in magnetic resonance imaging (MRI) studies examining structural and functional brain alterations [18�], which will be discussed in greater detail below.

In recent work, Tait and colleagues looked at young adult cannabis users (ages 20�) and found memory deficits, however cessation of cannabis use was associated with improved performance with abstinence over the course of eight years [21]. Takagi and colleagues found that cannabis users (ages 13�) performed worse on measures of immediate and delayed verbal memory compared to community controls. In a similar study by this team of investigators, no differences between cannabis users and community controls were found on measures of executive functioning [22, 23]. Similarly Gonzalez and colleagues (2012) found differences on immediate and delayed recall among young adult cannabis users (approximately age 20) compared to nonusing controls, however no differences were observed on measures of impulsivity. Despite no group differences on impulsivity, the authors found that worse performance on a decision making task was related to more cannabis use disorder symptoms [24]. Solowij and colleagues looked at 181 adolescents (ages 16�) and found that cannabis users performed worse on learning and recall, and poorer performance was related to severity, frequency, and age of initiation of cannabis use [25]. A study on prospective memory evaluated undergraduates between the ages of 18 and 24 years old, while no differences in self-reported prospective memory was identified, cannabis users did recall fewer location-action combinations during the objective video-based prospective memory task [26]. A large-scale (N = 1037) longitudinal investigation from New Zealand evaluating individuals from birth to age 38 recently found a decline in intelligence quotient, particularly executive functioning and processing speed, with persistent cannabis dependence. Notably, those individuals with weekly use before age 18 demonstrated greater decline in cognitive performance [27].

Early/Late Onset of Use

Studies evaluating early- and late-onset marijuana users have provided considerable insight into the effects of cannabis use on adolescent neurodevelopment. For example, Ehrenreich and colleagues (1999) found that initiation of marijuana use prior to age 16 predicted impaired reaction time on a task of sustained attentional processing [28]. In 2003, Pope and colleagues also found that early-onset (or initiation prior to age 17) was related to poorer performance on verbal memory and fluency tasks, as well as verbal IQ [29]. Focusing on executive functioning, Fontes and colleagues (2011) examined 104 chronic cannabis users ages 18�. All participants met criteria for DSM-IV cannabis abuse or dependence. The authors found that adolescent cannabis users reporting initiation prior to age 15 demonstrated poorer performance on tasks of sustained attention, impulse control, and executive functioning [30]. Overall, the majority of data support poorer cognitive performance on measures of attention and learning, and memory in adolescent users of cannabis, however frequency and severity of use is likely to play a role, particularly in those reporting younger age of initiation. Further, some evidence suggests that many of the subtle cognitive effects are likely to resolve after longer-term abstinence.

Structural NeuroImaging

Gray Matter Macrostructure

A large body of literature has shown dynamic changes in gray matter structures that are ongoing over adolescent development (e.g., cortical volume decline after about 6 years of age). For instance, dendritic pruning and elimination of synapses likely results in cortical thinning and decreased cerebral volume (e.g., subtraction of overproduced or weaker synaptic connections) to some degree, whereas some subcortical structures such as the hippocampus and amygdala have been shown to increase with age [2, 31, 32]. Studies show a high density of CB1 cannabinoid receptors in neocortex, hippocampus, amygdala, hypothalamus, basal ganglia, and cerebellum [4] therefore the degree to which cannabis use alters cortical and subcortical gray matter tissue development is being increasingly explored in the literature. While some alterations in gray matter macrostructure have been suggested, there has been inconsistent evidence of morphological changes as evaluated by structural MRI. For instance, Block and colleagues (1999) as well as DeLisi and colleagues (2006) found no differences in gray matter tissue volume between adolescent cannabis users and matched controls [33, 34].

In 2010, adolescent cannabis abusers (ages 16�) were found to have decreased right medial orbital prefrontal cortex volume compared to non-using counterparts volume was also found to be positively correlated with age of initiation of marijuana use in the sample (i.e., younger age of first use associated with reduced orbital prefrontal cortex volume) [35]. A second study published in 2010 found that while age was associated with changes in brain morphometry among non-users, there was no relationship between age and cortical gyrification in adolescent and young adult cannabis users. Cannabis users did show decreased concavity of the sulci and sulci thinning in frontal, temporal, and parietal lobes compared to non-users, highlighting the potential for cannabis to disrupt normal brain developmental trajectories [36]. Ashtari and colleagues (2011) found that heavy adolescent cannabis users abstinent for a minimum of 30 days had smaller bilateral hippocampal volumes compared to controls, and smaller right hippocampal volume was correlated with more self-reported cannabis use among users no between group differences were observed in amygdala volume [37].

Several studies from our laboratory evaluating abstinent adolescent cannabis users (approximately ages 16�) have found similar outcomes in regard to gray matter macrostructural changes. Medina and colleagues (2007 and 2009) found no difference in hippocampal volumes or prefrontal cortex volume in adolescent cannabis users compared to matched controls, despite observed differences in both hippocampus and prefrontal cortex in adolescent alcohol users compared to matched controls [19, 38]. We did observe a subtle gender interaction, as female cannabis users had a slightly larger prefrontal cortex compared to non-using female controls while this trend did not reach statistical significance, it may suggest that female marijuana users are more vulnerable to macrostructural alterations (given smaller prefrontal cortex volume was related to better executive functioning among users). Similarly, in 2011, amygdala volumes were compared between adolescent cannabis users and non-users. Findings suggest increased amygdala volume in female users compared to female non-users. Increased amygdala volume was associated with more self-reported depression and anxiety (internalizing) symptoms [39]. In a study investigating differences in cerebellum volumes, we found that adolescent marijuana users demonstrated larger inferior posterior vermis volume compared to controls larger cerebellar volume was associated with poorer executive functioning [40].

In recent investigation of temporal lobe structures, Cousijn and colleagues (2012) found that amygdala and hippocampal volume in a sample of young adults ages 18� correlated negatively with amount of cannabis use. Specifically, more weekly cannabis use in grams was related to smaller hippocampus volume in heavy users and increased severity of cannabis use was associated with smaller amygdala volume. The authors also found that anterior cerebellum volume was larger in adolescent heavy cannabis users compared to non-users [41].

A prospective study looking at gray matter volume at 12 years of age, prior to initiation of marijuana, found that smaller orbitofrontal cortex volume predicted initiation of cannabis use by 16 years of age, suggesting pre-existing structural abnormalities may play a role in both behavioral differences that lead to cannabis use as well as continued differences in the course of development [42].

There have been limited studies evaluating cortical thickness exclusively, however, Lopez-Larson (2011) evaluated 18 adolescents (ages 16�) with histories of heavy marijuana use (at least 100 marijuana use episodes in the past year) compared to non-using controls. Decreased cortical thickness was reported in the right caudal middle frontal and bilateral superior frontal cortices decreased thickness was also found in the bilateral insula. Marijuana users demonstrated increased cortical thickness in the bilateral lingual, right superior temporal, right parietal and left paracentral regions. Alterations of cortical thickness were related to severity of cannabis use and younger age of initiation of use in several brain regions. The authors suggest that marijuana may affect neurodevelopment (e.g., increased/decreased in cortical thickness) in two ways, 1) premature development and/or alterations in neurodevelopmental trajectories or 2) tissue loss or remodeling associated with marijuana-related toxicity [43].

Similar to findings by Lopez-Larson discussed above, the concept of deleterious effects related to early initiation of cannabis has been explored in the neuroimaging literature as well. According to Wilson and colleagues (2000), individuals reporting marijuana use prior to age 17 demonstrated decreased whole brain and cortical gray matter in addition to increased percent white matter volume. Findings also included higher cerebral blood flow in males reporting early initiation of marijuana use [44]. While findings do not necessarily support a clear and consistent pattern of changes in cortical/subcortical volume and thickness measurements, as emphasized by Lopez-Larson and colleagues, we can conclude that marijuana may influence the trajectories of appreciable gray matter changes in several ways. The compound may illicit premature tissue development, impose a marijuana-related effect on regressive changes (e.g., synaptic pruning, death of overproduced cells), and alter ongoing myelination of fiber tracts that are impacting gray matter estimates. Functional changes likely affect the mechanics that underlie structural brain changes, and interactions between these processes cannot be ruled out.

White Matter Microstructure

White matter tissue integrity (e.g., myelination, coherence of fiber tracts) is believed to be important for efficient cortical connectivity in the developing brain. The literature has shown linear increases in white matter over early development. As the brain becomes increasingly myelinated and fiber bundles mature from infancy to late adolescence, restriction of diffusing water molecules along the principal axis of an axon is commonly observed due to increasingly compact fibers and with more limited intracellular space [45, 46]. Diffusion tensor imaging (DTI) commonly utilizes two indices of white matter tract coherence to reflect water diffusion in white matter, fractional anisotropy (FA) and mean diffusivity (MD), which are thought to help to identify alterations in the health of white matter fibers. Increases in FA and decreases in MD are typically seen in healthy white matter development from young children to early adulthood [45]. In 2006, DeLisi and colleagues published one of the earlier studies to explore the potential for deleterious effects of cannabis on developing white matter.. The authors found higher FA and lower in MD in several tracts in MJ users compared to matched controls they conclude no evidence of pathological white matter changes despite finding differences between groups [33]. Since this study, findings do suggest some evidence of alterations in white matter integrity in adolescent cannabis users. While DeLisi and colleagues suggest no evidence of pathology per se, subsequent studies have since shown changes in unanticipated directions [47]. While this may not represent a typical pathological process, group differences in either direction may still be reflective of a neural alterations.

For instance, increased MD in the prefrontal fiber bundles of the corpus callosum in heavy cannabis using adults (daily use for more than two years) who initiated use during adolescence suggest changes in white matter development associated with cannabis use [48]. Ashtari and colleagues (2009) found that adolescents with heavy cannabis use enrolled in residential drug treatment had reduced FA and increased MD in cortical association areas such as the temporal-parietal fiber tracts [49]. Recently, in a small sample of adolescents approximately 18 years of age, WM alterations were found in cannabis users compared to controls. Decreased FA in cortical and subcortical areas (e.g., hippocampal projections, superior longitudinal fasciculus) was found in cannabis users (weekly use for at least one year) compared to controls with no history of substance abuse [50].

In our laboratory, we have found white matter alterations in our abstinent teen marijuana users (ages 16�) compared to controls. In two studies published in 2008 and 2009, we found poorer white matter integrity (e.g., decreased FA and increased MD) in several association and projection fiber tracts in adolescent cannabis users with concomitant alcohol use. Areas showing between group differences included tracts linked to fronto-parietal circuitry [51]. White matter integrity in several of these regions was linked to neurocognitive performance on measures of attention, working memory, and processing speed we have also seen white matter linked to emotional functioning and prospective risk taking in our substance users [52, 53]. To better understand microstructural differences in tissue integrity among adolescent marijuana users as compared to binge drinkers, we looked at white matter differences between adolescent binge drinkers compared to binge drinkers with histories of heavy marijuana use (ages 16�). While between group differences persisted between marijuana users and controls, surprisingly, teens engaging in binge drinking only looked significantly worse on indices of white matter integrity (i.e., decreased FA) in several areas (cortical and subcortical projection fibers) as compared to marijuana users, highlighting the need for further research to disentangle the effects of marijuana and alcohol on the developing brain [18].

In general, research points to poorer white matter integrity in adolescent marijuana users compared to non-substance using controls. While white matter findings are subtle in nature, we have observed poorer white matter integrity correlated with poorer neurocognitive functioning in our studies [47], which underscores the impact that slight alterations in white matter health during this time could have on optimal cognitive functioning. Interestingly, some preliminary evidence supports that marijuana-related toxicity on white matter integrity may be more modest compared to the impact adolescent alcohol use has on the developing brain, although more research in needed in this area.

Functional Imaging

FMRI Imaging

Changes in cognitive performance after acute and longer-term cannabis use are fairly well documented, even if residual effects are suspected to largely resolve. However, less is known on how brain functioning, or neural activation/signaling, may be changed by marijuana use and thereby reflected in declines in neuropsychological performance. Comparisons between blood oxygen dependent signal (BOLD) in adolescent marijuana users and controls in response to cognitive tasks have revealed subtle differences in brain activation patters in marijuana users. Jacobsen and colleagues (2004) were the first to pilot an auditory working memory (n-back) fMRI study comparing marijuana users (with tobacco use) compared to a tobacco using group and control group. The authors found cannabis users performed the task less accurately and failed to deactivate the right hippocampus across conditions. In another study by the same authors, nicotine withdrawal elicited increased activation across brain regions in the marijuana group, including parietal cortex, superior temporal gyrus, posterior cingulate gyrus, and the right hippocampus. The same effect was not found in the tobacco-only control group suggesting marijuana use may lead to developmental changes masked by nicotine use [54, 55]

We have conducted several BOLD fMRI studies evaluating differences in activation patters between our sample of abstinent marijuana users and matched controls. In 2007, we found marijuana users to have substantially more activation than non-using peers in response to an inhibitory processing task, particularly in parietal and dorsolateral prefrontal cortices, suggesting additional neural resources required to maintain adequate executive control during response inhibition [56]. In evaluating response patterns to a spatial working memory task, adolescent marijuana users exhibited increased activation in the right parietal lobe along with diminished activation in the right dorsolateral prefrontal cortex to achieve good task performance, which was not observed in controls [57, 58]. In a follow-up investigation using the same spatial working memory task, we evaluated teens with more recent abstinence (2𠄷 days abstinent) compared to prolonged abstinence (27�) from marijuana, as well as matched controls. Recent users showed greater brain activation in prefrontal cortices, regions needed for working memory processes, and bilateral insula [59]. In response to a third task assessing verbal encoding, marijuana users demonstrated increased encoding-related activation in anterior brain regions as compared to decreased activation in posterior regions, despite no differences in task performance [20] findings may suggest increased recruitment of neural resources in brain areas subserving task-related processing in marijuana using teens.

Several recent studies outside of our laboratory have shown similar findings. For example, Jager and colleagues (2010) evaluated boys with frequent cannabis use (more than 200 lifetime cannabis use episodes) compared to matched controls (ages 13�) and found that cannabis users showed excessive activity in prefrontal regions in response to a working memory task [60], studies from this same research group with young adults have yielded similar, although modest, aberrant findings of the working memory system [61]. In 2010, an investigation comprising chronic marijuana users and matched controls (approximately 19 years old), suggest increased activity in the prefrontal cortex in response to a task requiring executive aspects of attention [62]. Cousijn and colleauges recently found increased activation in heavy cannabis users (ages 18�) in response to the Iowa Gambling task during win evaluations in brain areas such as the insula, caudate, and temporal gyrus, which was also positively related to weekly cannabis use win-related increase in brain activity also predicted increased cannabis use six months later [63] Lopez-Larson and colleagues (2012) found differences in cortico-cerebellar activity in older adolescents with heavy marijuana use. The authors describe decreased activation in response to a bilateral finger-tapping task, and motor function activation was negatively correlated with total lifetime marijuana use [64]. Age of onset also continues to play an important role, as early-onset cannabis users (prior to age 16) demonstrated increased activation in the left superior parietal lobe in response to a verbal working memory challenge (verbal n-back task), and earlier initiation of use was associated with increased BOLD activity [65]. The majority of findings suggest increased recruitment of neural resources (possibly reflecting compensation or changes in the efficiency of strategic neural processing) in brain areas subserving task-related processing in marijuana using teens.

Electroencephalogram (EEG)

There has been limited research on brain functioning using EEG among adolescent cannabis users. The strength in using EEG is the degree of temporal resolution that is not possible with BOLD imaging. Information on the degree of attentional bias to marijuana cues may provide some indication of brain-based differences in cue-reactivity resulting in heavier use of marijuana among certain teenagers. For instance, one lab based paradigm of cue reactivity found increased skin conductivity among teens diagnosed with cannabis use disorder [66]. Nickerson and colleagues (2011) found that among adolescents ages 14�, P300 response (i.e., event-related potential response) was larger among cannabis users, and response increased (along with craving) in the user group after handling marijuana paraphernalia findings suggest attentional bias, increased arousal, and possible neural differences (either pre-existing or altered by ongoing substance use engagement) that may elucidate discrepancies among teen substance use engagement [67].

Blood Perfusion

The neurovascular effect of marijuana use in adolescence has not been studied extensively. Understanding vascular changes in cerebral blood flow (CBF) can help us better understand neural signaling and vascular alterations that may be related to changes in neurocognitive functioning and/or changes in neural signaling related to the BOLD signal. Adult studies typically report increased CBF after acute exposure and lower or stabilized CBF after a period of abstinence in heavy users, although this has varied to some degree [68�].

To our knowledge, there has only been one study in adolescent blood perfusion in heavy cannabis users. In a recent study in our laboratory utilizing arterial spin labeling (ASL), we found that heavy marijuana users (approximately 17 years old) assessed pre-and post 28 days of monitored abstinence showed reduced CBF in 4 cortical regions, including the left superior and middle temporal gyri, left insula, left and right medial frontal gyrus, and left supramarginal gyrus at baseline users showed increased CBF in the right precuneus at baseline, as compared to controls. We did not observe group differences in neurovascular functioning after four weeks of abstinence, suggesting marijuana may influence cerebral blood flow acutely with a possible return to baseline with prolonged abstinence [71]. A study evaluating young adults (age range 21�) found that acute THC administration increased blood perfusion in areas important for emotional and cognitive processing, such as the anterior cingulate, frontal cortex, and insula, and reduced perfusion in posterior brain regions. Resting state activity was also altered, as THC increased baseline activity [72].

Magnetic Resonance Spectroscopy

Very few studies have looked at neurochemical brain changes related to marijuana use in adolescence. Prescott and colleagues (2011) found decreases in metabolite concentrations (e.g., glutamate and N-acetyl aspartate) in the anterior cingulate, suggesting poorer underlying neuronal health in adolescent marijuana users [73], While the exact mechanisms by which cannabis would affect neuronal health is unclear, it is possible that modulation of neurotransmitters such as glutamate and GABA have adverse consequences on cellular development and neuron integrity changes in neuronal health is one suggested mechanism which may underlie neuroimaging and neurocognitive findings discussed above.

Preclinical Studies

A fairly large amount of work can be found on animal models of adolescent cannabis exposure. A detailed analysis of the preclinical studies is beyond the scope of this review, however briefly discussing the existing literature is important for translation to human models. Studies also focus on various cannabinoids beside Δ 9 -tetrahydrocannabinol (Δ 9 -THC), the principal psychoactive component of marijuana for example increasing attention is being given to cannabidiol, a nonpsychoactive cannabinoid that may have promising therapeutic effects independent of THC [74]. However, this brief summary will focus on models of exposure to the natural compound or cannabinoid agonists, which mimic the structure Δ 9 -THC. A great benefit of animal studies is lack of heterogeneity that corresponds with human consumption and substance use reporting.

In animals, postnatal days 28� correspond with human adolescent development (which can range from 21� for inclusion of early/late adolescent development) [75]. Studies during this postnatal time period in rats have evaluated both emotional behavior as well as cognitive/behavioral functioning. One of the first research groups to look at cannabinoid exposure found poorer performance on cognitive tasks, such as maze learning, in immature rats compared to mature rats treated with THC [76]. Schneider and Koch (2003, 2005) have reported alterations in pubertal rats treated with the receptor agonist WIN, discrepancies in performance range from sensorimotor functioning, object recognition memory, and social behavior [77, 78]. A more recent study by Schneider and colleagues (2008) found that chronic WIN treated pubertal rats demonstrated object/social recognition deficits, which the authors suggest is consistent with impairment in short-term information processing. Particularly, immature animals demonstrated more pronounced behavioral alterations as compared to mature animals after acute exposure to WIN, and more lasting deficits in social play and grooming behaviors [79]. Deficits in object recognition have also been reported in male and female pubertal rats treated with a cannabinoid receptor agonist as well as THC [80�], and there is some support that findings are consistent across age groups.

Spatial functioning in adolescent rats has also shown affected by acute THC treatment [83]. In a recent investigation by Abush and colleagues (2012), chronic WIN treatment was found to result in both acute and longer term effects not only in spatial memory and object recognition, but interestingly, long term potentiation in areas such as nucleus accumbens pathways [84]. Studies are actively evaluating emotional functioning and neurochemical transmission in adolescent animals after exposure to cannabinoid agonists, as well as how cannabinoids moderate state-dependent learning based on brain regions [85, 86]. While this is not an exhaustive review of the preclinical findings, in general, the data suggest differential and often negative impact on adolescent animals compared to adult animals exposed to THC or other cannabinoid agonists in behavioral, emotional, and social outcomes. The animal work is particularly important to highlight, given the consistency in many adolescent neurocognitive and neuroimaging studies conducted with human subjects reporting regular use of marijuana, as the findings often point to the deleterious effects on brain functioning compared to non-using controls.


(Dis)Connected

Apple introduced its iPhone in 2007 and the world has never been the same. Though the iPhone wasn't the first internet-enabled "smartphone," its touchscreen technology and built-in app library made it the first to gain mass-market appeal—and it sparked a revolution. Now, wireless mobile devices have found their way into millions of pockets, functioning not just as phones but as internet browsers, messaging services, calendars, cameras, alarm clocks, road maps and video players.

By 2015, 72 percent of U.S. adults reported owning a smartphone, according to a study by the Pew Research Center (Pew, 2016). Smartphones undoubtedly make our lives easier, says Elizabeth Dunn, PhD, a psychology professor at the University of British Columbia, who studies the ways that mobile technology can support or undermine well-being. "Having the entire store of human knowledge at our fingertips is pretty useful," she quips. But there may be trade-offs for that convenience. Mobile technology also has the power to negatively influence our health andhappiness, she says. "Our lab has gone looking for pros, but in general we keep finding cons."

At their worst, research finds smartphones can mess with our sleep, stress us out and monopolize our attention. But psychology may hold the key to helping people take control of this technology to prevent such negative effects and even enhance our well-being. Tech developers aren't in the business of promoting well-being, Dunn says, but that does fall under the purview of psychology. "I think we need to devote a lot more attention to this," she says.


Media Use Is Linked to Lower Psychological Well-Being: Evidence from Three Datasets

Adolescents spend a substantial and increasing amount of time using digital media (smartphones, computers, social media, gaming, Internet), but existing studies do not agree on whether time spent on digital media is associated with lower psychological well-being (including happiness, general well-being, and indicators of low well-being such as depression, suicidal ideation, and suicide attempts). Across three large surveys of adolescents in two countries (n = 221,096), light users (<1 h a day) of digital media reported substantially higher psychological well-being than heavy users (5+ hours a day). Datasets initially presented as supporting opposite conclusions produced similar effect sizes when analyzed using the same strategy. Heavy users (vs. light) of digital media were 48% to 171% more likely to be unhappy, to be in low in well-being, or to have suicide risk factors such as depression, suicidal ideation, or past suicide attempts. Heavy users (vs. light) were twice as likely to report having attempted suicide. Light users (rather than non- or moderate users) were highest in well-being, and for most digital media use the largest drop in well-being occurred between moderate use and heavy use. The limitations of using percent variance explained as a gauge of practical impact are discussed.

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How to Break a Cell Phone Addiction

Whether you meet the criteria for a full-blown phone addiction or simply want to reduce your emotional dependency on technology, there are lots of helpful strategies for breaking this bad habit.

Outsmart your smartphone by using technology to limit your technology use. Want to use your phone less? There’s an app for that. In fact, there are LOTS of apps for that. The BreakFree app, for instance monitors your phone usage, tallying up the number of times your unlock the screen, how many minutes hours you spend on your phone, which apps you use the most. The app then gives you a daily addiction score. If your addiction score alone isn’t motivation enough to make you think twice before using your phone, the app also allows you to set up notifications to alert you when you’ve been on your phone for an extended period of time or opened an app too many times.

Get your phone out of the bedroom. There are lots of reasons why you should not sleep with your phone. For starters, using your phone within an hour of bedtime leads to poorer sleep quality and more insomnia. If you’re like me and you check your phone every time you wake up in the night, your sleep is even more negatively impacted. Furthermore, when you wake up and check your phone before getting out of bed, you are reinforcing the habit for the rest of the day. Buy a cheap alarm clock and stop sleeping with your phone by your side.

Put yourself on a digital diet. The same way reducing your waistline involves breaking unhealthy habits and eating more mindfully, reducing your screen time requires similar self-control. When you want to lose weight, you have to stop eating the junk food. When you want to cut back on smartphone use, you have to stop using the junk apps. Delete those deliciously addictive games. Cut back on social networks the way a nutritionist might suggest you cut back on carbs. Quitting technology cold turkey isn’t a realistic option for most people, so this requires some real will-power. Temporarily (if not permanently) deleting your most frequently used apps can be a huge help.

Set up a digital schedule. Assign certain chunks of time throughout the day to go phone free. Experiment with leaving your phone at home when you go to dinner with your friends. Turn your phone off for a couple hours every day at the office so you can work without distraction. Leave your phone in the other room in the evenings in order to spend more quality time with your partner or children.

Get drastic with a digital detox. If you are open to trying something more extreme, Daniel Sieberg, author of The Digital Diet: The 4-Step Plan to Break Your Tech Addiction and Regain Balance in Your Life, suggests doing a full “digital detox,” where you spend an entire weekend with ZERO access to technology. Notify your loved ones in advance, power your devices off and stick them in a box or a bottom drawer, and ask a trusted friend to temporarily change your passwords to reduce temptation. After the detox, Sieberg suggests reintroducing technology slowly. He swears that a digital diet does wonders for reconnecting with the real world and improving relationships.


Introduction

According to the 2011 Monitoring the Future Study, marijuana remains the most commonly used illicit drug in adolescence in the United States, one of few increasing in prevalence. In fact, marijuana has been the most commonly used illicit substance for almost 40 years, and presently 23% of 12 th graders in the U.S. report using marijuana in the past month [1]. Marijuana use in adolescence could have implications for academic functioning, as well as social and occupational functioning extending into later life. Maturational brain changes, particularly myelination and synaptic pruning, are occurring throughout adolescence, well into early adulthood [2]. These remodeling processes are purportedly linked to efficient neural processing, and believed to underlie specialized cognitive processing necessary for optimal neurocognitive performance.

Cannabinoid receptors (CB1) are widely distributed throughout the brain (e.g., hippocampus, prefrontal cortex), and play a role in neurotransmitter release and concentrations across neural systems (excitatory and inhibitory). It has been suggested that these receptors increase during adolescence, have a role in genetic expression of neural development, and that alteration of the endocannabinoid system during adolescence may results in a cascade of neurochemical and neurostructural aberrations, thus leading to poorer cognitive and emotional outcomes in adulthood [3, 4].

Disruptions in brain development related to neurotoxic effects of regular marijuana use could significantly alter neurodevelopmental trajectories by not only changing neurochemical communication and genetic expression of neural development, but causing a toxic effect on brain tissue. Such a marijuana-related effect on white matter and gray matter structures (e.g., changes in myelin, axons, and synapses) could have widespread implications for healthy brain development from childhood to young adulthood on subtle cognitive functioning and success in daily functioning. Studies exploring the neurocognitive consequences and structural and functional neuroimaging changes related to marijuana use in adolescence will be discussed, along with recommendations for future work.

Cognition

Adult studies of marijuana use often find subtle decreases in performance compared to controls in cognitive domains such as attention, memory, and processing speed such effects have been discussed as transient in the literature given limited group differences after prolonged abstinence from marijuana [5, 6]. It is unclear if findings translate to adolescent populations. Ongoing cognitive development in the domains of memory and executive functioning, and particularly in specialized functions like cognitive control, is not only tightly associated with adolescence and neocortical tissue maturation, but is likely to have implications for school performance and engagement in risk/reward behaviors [7]. One of the earliest studies on the effects of marijuana on adolescent neurocognitive development evaluated verbal and nonverbal memory performance in cannabis-dependent adolescents (ages 14 to 16) compared to matched controls [8]. Schwartz and colleagues found that short-term memory impairment persisted after six weeks of monitored abstinence. In contrast, Teichner and colleagues (2000) found no relationship between marijuana use severity and cognitive performance among cognitively impaired and unimpaired adolescents referred for drug treatment [9]. There have been considerable additions to the literature over the last decade, yet the degree of impairment related to marijuana use in adolescence remains inconclusive. A pattern of subtle yet potentially detrimental effects in cognitive domains related to attention, learning, and memory are most often described.

A prospective study conducted in 2005 examined neurocognitive performance among 17� year olds with history of soft drug exposure in utero compared to prior performance at 9� years old. Current heavy cannabis users performed significantly worse on measures of processing speed and memory, controlling for pre-drug performance. Notably, former heavy users (reporting 3 months without regular use) had similar scores to non-marijuana using controls [10]. In regard to higher-order cognitive functioning, Lane and colleagues (2007) found adolescents (ages 14�) with histories of heavy marijuana use performed worse on perseverative responding and flexible thinking compared to controls with limited histories of use. This same research group also found evidence of reduced motivation among marijuana users compared to controls [11, 12]. In 2007, Harvey and colleagues found adolescent marijuana users (age 13� use greater than once per week) performed worse on tests of attention, learning, and memory furthermore, poorer performance on executive functioning in this sample was related to more days of cannabis use in the past month [13].

Studies from our laboratory have largely found differences in similar domains following four weeks of monitored abstinence. Our first prospective investigation found that cumulative marijuana use over the course of eight years (teens followed from ages 13�) was related to poorer performance on measures of attentional functioning [14]. In a subsequent cross-sectional study of adolescent marijuana users ages 16�, we found that marijuana users demonstrated slower processing speed, poorer verbal learning and memory, and sequencing abilities [15]. In order to better understand acute changes with abstinence, we examined neurocognitive performance over 3 weeks of monitored abstinence in marijuana users ages 15�. Between-group differences in attention, learning, and memory were identified at baseline, however while learning and memory performance reached similar levels of performance to controls after 3 weeks of abstinence, attention differences persisted [16].

Group differences in our studies generally persist despite controlling for alcohol use present in both controls and marijuana users but to further understand differential contributions of marijuana and alcohol to neurocognitive functioning in our sample, we examined unique associations between alcohol use severity and cognitive functioning in both marijuana users as well as controls. In a recent investigation, we found that more self-reported alcohol withdrawal symptoms predicted poorer performance on learning and memory in a sample of non-marijuana using teens with histories of episodic alcohol use, despite no relationship in our marijuana users with similar and/or heavier self-reported history of alcohol use [17]. This suggests differential relationships between marijuana, alcohol, and cognitive outcomes in our sample. We have observed similar relationships in magnetic resonance imaging (MRI) studies examining structural and functional brain alterations [18�], which will be discussed in greater detail below.

In recent work, Tait and colleagues looked at young adult cannabis users (ages 20�) and found memory deficits, however cessation of cannabis use was associated with improved performance with abstinence over the course of eight years [21]. Takagi and colleagues found that cannabis users (ages 13�) performed worse on measures of immediate and delayed verbal memory compared to community controls. In a similar study by this team of investigators, no differences between cannabis users and community controls were found on measures of executive functioning [22, 23]. Similarly Gonzalez and colleagues (2012) found differences on immediate and delayed recall among young adult cannabis users (approximately age 20) compared to nonusing controls, however no differences were observed on measures of impulsivity. Despite no group differences on impulsivity, the authors found that worse performance on a decision making task was related to more cannabis use disorder symptoms [24]. Solowij and colleagues looked at 181 adolescents (ages 16�) and found that cannabis users performed worse on learning and recall, and poorer performance was related to severity, frequency, and age of initiation of cannabis use [25]. A study on prospective memory evaluated undergraduates between the ages of 18 and 24 years old, while no differences in self-reported prospective memory was identified, cannabis users did recall fewer location-action combinations during the objective video-based prospective memory task [26]. A large-scale (N = 1037) longitudinal investigation from New Zealand evaluating individuals from birth to age 38 recently found a decline in intelligence quotient, particularly executive functioning and processing speed, with persistent cannabis dependence. Notably, those individuals with weekly use before age 18 demonstrated greater decline in cognitive performance [27].

Early/Late Onset of Use

Studies evaluating early- and late-onset marijuana users have provided considerable insight into the effects of cannabis use on adolescent neurodevelopment. For example, Ehrenreich and colleagues (1999) found that initiation of marijuana use prior to age 16 predicted impaired reaction time on a task of sustained attentional processing [28]. In 2003, Pope and colleagues also found that early-onset (or initiation prior to age 17) was related to poorer performance on verbal memory and fluency tasks, as well as verbal IQ [29]. Focusing on executive functioning, Fontes and colleagues (2011) examined 104 chronic cannabis users ages 18�. All participants met criteria for DSM-IV cannabis abuse or dependence. The authors found that adolescent cannabis users reporting initiation prior to age 15 demonstrated poorer performance on tasks of sustained attention, impulse control, and executive functioning [30]. Overall, the majority of data support poorer cognitive performance on measures of attention and learning, and memory in adolescent users of cannabis, however frequency and severity of use is likely to play a role, particularly in those reporting younger age of initiation. Further, some evidence suggests that many of the subtle cognitive effects are likely to resolve after longer-term abstinence.

Structural NeuroImaging

Gray Matter Macrostructure

A large body of literature has shown dynamic changes in gray matter structures that are ongoing over adolescent development (e.g., cortical volume decline after about 6 years of age). For instance, dendritic pruning and elimination of synapses likely results in cortical thinning and decreased cerebral volume (e.g., subtraction of overproduced or weaker synaptic connections) to some degree, whereas some subcortical structures such as the hippocampus and amygdala have been shown to increase with age [2, 31, 32]. Studies show a high density of CB1 cannabinoid receptors in neocortex, hippocampus, amygdala, hypothalamus, basal ganglia, and cerebellum [4] therefore the degree to which cannabis use alters cortical and subcortical gray matter tissue development is being increasingly explored in the literature. While some alterations in gray matter macrostructure have been suggested, there has been inconsistent evidence of morphological changes as evaluated by structural MRI. For instance, Block and colleagues (1999) as well as DeLisi and colleagues (2006) found no differences in gray matter tissue volume between adolescent cannabis users and matched controls [33, 34].

In 2010, adolescent cannabis abusers (ages 16�) were found to have decreased right medial orbital prefrontal cortex volume compared to non-using counterparts volume was also found to be positively correlated with age of initiation of marijuana use in the sample (i.e., younger age of first use associated with reduced orbital prefrontal cortex volume) [35]. A second study published in 2010 found that while age was associated with changes in brain morphometry among non-users, there was no relationship between age and cortical gyrification in adolescent and young adult cannabis users. Cannabis users did show decreased concavity of the sulci and sulci thinning in frontal, temporal, and parietal lobes compared to non-users, highlighting the potential for cannabis to disrupt normal brain developmental trajectories [36]. Ashtari and colleagues (2011) found that heavy adolescent cannabis users abstinent for a minimum of 30 days had smaller bilateral hippocampal volumes compared to controls, and smaller right hippocampal volume was correlated with more self-reported cannabis use among users no between group differences were observed in amygdala volume [37].

Several studies from our laboratory evaluating abstinent adolescent cannabis users (approximately ages 16�) have found similar outcomes in regard to gray matter macrostructural changes. Medina and colleagues (2007 and 2009) found no difference in hippocampal volumes or prefrontal cortex volume in adolescent cannabis users compared to matched controls, despite observed differences in both hippocampus and prefrontal cortex in adolescent alcohol users compared to matched controls [19, 38]. We did observe a subtle gender interaction, as female cannabis users had a slightly larger prefrontal cortex compared to non-using female controls while this trend did not reach statistical significance, it may suggest that female marijuana users are more vulnerable to macrostructural alterations (given smaller prefrontal cortex volume was related to better executive functioning among users). Similarly, in 2011, amygdala volumes were compared between adolescent cannabis users and non-users. Findings suggest increased amygdala volume in female users compared to female non-users. Increased amygdala volume was associated with more self-reported depression and anxiety (internalizing) symptoms [39]. In a study investigating differences in cerebellum volumes, we found that adolescent marijuana users demonstrated larger inferior posterior vermis volume compared to controls larger cerebellar volume was associated with poorer executive functioning [40].

In recent investigation of temporal lobe structures, Cousijn and colleagues (2012) found that amygdala and hippocampal volume in a sample of young adults ages 18� correlated negatively with amount of cannabis use. Specifically, more weekly cannabis use in grams was related to smaller hippocampus volume in heavy users and increased severity of cannabis use was associated with smaller amygdala volume. The authors also found that anterior cerebellum volume was larger in adolescent heavy cannabis users compared to non-users [41].

A prospective study looking at gray matter volume at 12 years of age, prior to initiation of marijuana, found that smaller orbitofrontal cortex volume predicted initiation of cannabis use by 16 years of age, suggesting pre-existing structural abnormalities may play a role in both behavioral differences that lead to cannabis use as well as continued differences in the course of development [42].

There have been limited studies evaluating cortical thickness exclusively, however, Lopez-Larson (2011) evaluated 18 adolescents (ages 16�) with histories of heavy marijuana use (at least 100 marijuana use episodes in the past year) compared to non-using controls. Decreased cortical thickness was reported in the right caudal middle frontal and bilateral superior frontal cortices decreased thickness was also found in the bilateral insula. Marijuana users demonstrated increased cortical thickness in the bilateral lingual, right superior temporal, right parietal and left paracentral regions. Alterations of cortical thickness were related to severity of cannabis use and younger age of initiation of use in several brain regions. The authors suggest that marijuana may affect neurodevelopment (e.g., increased/decreased in cortical thickness) in two ways, 1) premature development and/or alterations in neurodevelopmental trajectories or 2) tissue loss or remodeling associated with marijuana-related toxicity [43].

Similar to findings by Lopez-Larson discussed above, the concept of deleterious effects related to early initiation of cannabis has been explored in the neuroimaging literature as well. According to Wilson and colleagues (2000), individuals reporting marijuana use prior to age 17 demonstrated decreased whole brain and cortical gray matter in addition to increased percent white matter volume. Findings also included higher cerebral blood flow in males reporting early initiation of marijuana use [44]. While findings do not necessarily support a clear and consistent pattern of changes in cortical/subcortical volume and thickness measurements, as emphasized by Lopez-Larson and colleagues, we can conclude that marijuana may influence the trajectories of appreciable gray matter changes in several ways. The compound may illicit premature tissue development, impose a marijuana-related effect on regressive changes (e.g., synaptic pruning, death of overproduced cells), and alter ongoing myelination of fiber tracts that are impacting gray matter estimates. Functional changes likely affect the mechanics that underlie structural brain changes, and interactions between these processes cannot be ruled out.

White Matter Microstructure

White matter tissue integrity (e.g., myelination, coherence of fiber tracts) is believed to be important for efficient cortical connectivity in the developing brain. The literature has shown linear increases in white matter over early development. As the brain becomes increasingly myelinated and fiber bundles mature from infancy to late adolescence, restriction of diffusing water molecules along the principal axis of an axon is commonly observed due to increasingly compact fibers and with more limited intracellular space [45, 46]. Diffusion tensor imaging (DTI) commonly utilizes two indices of white matter tract coherence to reflect water diffusion in white matter, fractional anisotropy (FA) and mean diffusivity (MD), which are thought to help to identify alterations in the health of white matter fibers. Increases in FA and decreases in MD are typically seen in healthy white matter development from young children to early adulthood [45]. In 2006, DeLisi and colleagues published one of the earlier studies to explore the potential for deleterious effects of cannabis on developing white matter.. The authors found higher FA and lower in MD in several tracts in MJ users compared to matched controls they conclude no evidence of pathological white matter changes despite finding differences between groups [33]. Since this study, findings do suggest some evidence of alterations in white matter integrity in adolescent cannabis users. While DeLisi and colleagues suggest no evidence of pathology per se, subsequent studies have since shown changes in unanticipated directions [47]. While this may not represent a typical pathological process, group differences in either direction may still be reflective of a neural alterations.

For instance, increased MD in the prefrontal fiber bundles of the corpus callosum in heavy cannabis using adults (daily use for more than two years) who initiated use during adolescence suggest changes in white matter development associated with cannabis use [48]. Ashtari and colleagues (2009) found that adolescents with heavy cannabis use enrolled in residential drug treatment had reduced FA and increased MD in cortical association areas such as the temporal-parietal fiber tracts [49]. Recently, in a small sample of adolescents approximately 18 years of age, WM alterations were found in cannabis users compared to controls. Decreased FA in cortical and subcortical areas (e.g., hippocampal projections, superior longitudinal fasciculus) was found in cannabis users (weekly use for at least one year) compared to controls with no history of substance abuse [50].

In our laboratory, we have found white matter alterations in our abstinent teen marijuana users (ages 16�) compared to controls. In two studies published in 2008 and 2009, we found poorer white matter integrity (e.g., decreased FA and increased MD) in several association and projection fiber tracts in adolescent cannabis users with concomitant alcohol use. Areas showing between group differences included tracts linked to fronto-parietal circuitry [51]. White matter integrity in several of these regions was linked to neurocognitive performance on measures of attention, working memory, and processing speed we have also seen white matter linked to emotional functioning and prospective risk taking in our substance users [52, 53]. To better understand microstructural differences in tissue integrity among adolescent marijuana users as compared to binge drinkers, we looked at white matter differences between adolescent binge drinkers compared to binge drinkers with histories of heavy marijuana use (ages 16�). While between group differences persisted between marijuana users and controls, surprisingly, teens engaging in binge drinking only looked significantly worse on indices of white matter integrity (i.e., decreased FA) in several areas (cortical and subcortical projection fibers) as compared to marijuana users, highlighting the need for further research to disentangle the effects of marijuana and alcohol on the developing brain [18].

In general, research points to poorer white matter integrity in adolescent marijuana users compared to non-substance using controls. While white matter findings are subtle in nature, we have observed poorer white matter integrity correlated with poorer neurocognitive functioning in our studies [47], which underscores the impact that slight alterations in white matter health during this time could have on optimal cognitive functioning. Interestingly, some preliminary evidence supports that marijuana-related toxicity on white matter integrity may be more modest compared to the impact adolescent alcohol use has on the developing brain, although more research in needed in this area.

Functional Imaging

FMRI Imaging

Changes in cognitive performance after acute and longer-term cannabis use are fairly well documented, even if residual effects are suspected to largely resolve. However, less is known on how brain functioning, or neural activation/signaling, may be changed by marijuana use and thereby reflected in declines in neuropsychological performance. Comparisons between blood oxygen dependent signal (BOLD) in adolescent marijuana users and controls in response to cognitive tasks have revealed subtle differences in brain activation patters in marijuana users. Jacobsen and colleagues (2004) were the first to pilot an auditory working memory (n-back) fMRI study comparing marijuana users (with tobacco use) compared to a tobacco using group and control group. The authors found cannabis users performed the task less accurately and failed to deactivate the right hippocampus across conditions. In another study by the same authors, nicotine withdrawal elicited increased activation across brain regions in the marijuana group, including parietal cortex, superior temporal gyrus, posterior cingulate gyrus, and the right hippocampus. The same effect was not found in the tobacco-only control group suggesting marijuana use may lead to developmental changes masked by nicotine use [54, 55]

We have conducted several BOLD fMRI studies evaluating differences in activation patters between our sample of abstinent marijuana users and matched controls. In 2007, we found marijuana users to have substantially more activation than non-using peers in response to an inhibitory processing task, particularly in parietal and dorsolateral prefrontal cortices, suggesting additional neural resources required to maintain adequate executive control during response inhibition [56]. In evaluating response patterns to a spatial working memory task, adolescent marijuana users exhibited increased activation in the right parietal lobe along with diminished activation in the right dorsolateral prefrontal cortex to achieve good task performance, which was not observed in controls [57, 58]. In a follow-up investigation using the same spatial working memory task, we evaluated teens with more recent abstinence (2𠄷 days abstinent) compared to prolonged abstinence (27�) from marijuana, as well as matched controls. Recent users showed greater brain activation in prefrontal cortices, regions needed for working memory processes, and bilateral insula [59]. In response to a third task assessing verbal encoding, marijuana users demonstrated increased encoding-related activation in anterior brain regions as compared to decreased activation in posterior regions, despite no differences in task performance [20] findings may suggest increased recruitment of neural resources in brain areas subserving task-related processing in marijuana using teens.

Several recent studies outside of our laboratory have shown similar findings. For example, Jager and colleagues (2010) evaluated boys with frequent cannabis use (more than 200 lifetime cannabis use episodes) compared to matched controls (ages 13�) and found that cannabis users showed excessive activity in prefrontal regions in response to a working memory task [60], studies from this same research group with young adults have yielded similar, although modest, aberrant findings of the working memory system [61]. In 2010, an investigation comprising chronic marijuana users and matched controls (approximately 19 years old), suggest increased activity in the prefrontal cortex in response to a task requiring executive aspects of attention [62]. Cousijn and colleauges recently found increased activation in heavy cannabis users (ages 18�) in response to the Iowa Gambling task during win evaluations in brain areas such as the insula, caudate, and temporal gyrus, which was also positively related to weekly cannabis use win-related increase in brain activity also predicted increased cannabis use six months later [63] Lopez-Larson and colleagues (2012) found differences in cortico-cerebellar activity in older adolescents with heavy marijuana use. The authors describe decreased activation in response to a bilateral finger-tapping task, and motor function activation was negatively correlated with total lifetime marijuana use [64]. Age of onset also continues to play an important role, as early-onset cannabis users (prior to age 16) demonstrated increased activation in the left superior parietal lobe in response to a verbal working memory challenge (verbal n-back task), and earlier initiation of use was associated with increased BOLD activity [65]. The majority of findings suggest increased recruitment of neural resources (possibly reflecting compensation or changes in the efficiency of strategic neural processing) in brain areas subserving task-related processing in marijuana using teens.

Electroencephalogram (EEG)

There has been limited research on brain functioning using EEG among adolescent cannabis users. The strength in using EEG is the degree of temporal resolution that is not possible with BOLD imaging. Information on the degree of attentional bias to marijuana cues may provide some indication of brain-based differences in cue-reactivity resulting in heavier use of marijuana among certain teenagers. For instance, one lab based paradigm of cue reactivity found increased skin conductivity among teens diagnosed with cannabis use disorder [66]. Nickerson and colleagues (2011) found that among adolescents ages 14�, P300 response (i.e., event-related potential response) was larger among cannabis users, and response increased (along with craving) in the user group after handling marijuana paraphernalia findings suggest attentional bias, increased arousal, and possible neural differences (either pre-existing or altered by ongoing substance use engagement) that may elucidate discrepancies among teen substance use engagement [67].

Blood Perfusion

The neurovascular effect of marijuana use in adolescence has not been studied extensively. Understanding vascular changes in cerebral blood flow (CBF) can help us better understand neural signaling and vascular alterations that may be related to changes in neurocognitive functioning and/or changes in neural signaling related to the BOLD signal. Adult studies typically report increased CBF after acute exposure and lower or stabilized CBF after a period of abstinence in heavy users, although this has varied to some degree [68�].

To our knowledge, there has only been one study in adolescent blood perfusion in heavy cannabis users. In a recent study in our laboratory utilizing arterial spin labeling (ASL), we found that heavy marijuana users (approximately 17 years old) assessed pre-and post 28 days of monitored abstinence showed reduced CBF in 4 cortical regions, including the left superior and middle temporal gyri, left insula, left and right medial frontal gyrus, and left supramarginal gyrus at baseline users showed increased CBF in the right precuneus at baseline, as compared to controls. We did not observe group differences in neurovascular functioning after four weeks of abstinence, suggesting marijuana may influence cerebral blood flow acutely with a possible return to baseline with prolonged abstinence [71]. A study evaluating young adults (age range 21�) found that acute THC administration increased blood perfusion in areas important for emotional and cognitive processing, such as the anterior cingulate, frontal cortex, and insula, and reduced perfusion in posterior brain regions. Resting state activity was also altered, as THC increased baseline activity [72].

Magnetic Resonance Spectroscopy

Very few studies have looked at neurochemical brain changes related to marijuana use in adolescence. Prescott and colleagues (2011) found decreases in metabolite concentrations (e.g., glutamate and N-acetyl aspartate) in the anterior cingulate, suggesting poorer underlying neuronal health in adolescent marijuana users [73], While the exact mechanisms by which cannabis would affect neuronal health is unclear, it is possible that modulation of neurotransmitters such as glutamate and GABA have adverse consequences on cellular development and neuron integrity changes in neuronal health is one suggested mechanism which may underlie neuroimaging and neurocognitive findings discussed above.

Preclinical Studies

A fairly large amount of work can be found on animal models of adolescent cannabis exposure. A detailed analysis of the preclinical studies is beyond the scope of this review, however briefly discussing the existing literature is important for translation to human models. Studies also focus on various cannabinoids beside Δ 9 -tetrahydrocannabinol (Δ 9 -THC), the principal psychoactive component of marijuana for example increasing attention is being given to cannabidiol, a nonpsychoactive cannabinoid that may have promising therapeutic effects independent of THC [74]. However, this brief summary will focus on models of exposure to the natural compound or cannabinoid agonists, which mimic the structure Δ 9 -THC. A great benefit of animal studies is lack of heterogeneity that corresponds with human consumption and substance use reporting.

In animals, postnatal days 28� correspond with human adolescent development (which can range from 21� for inclusion of early/late adolescent development) [75]. Studies during this postnatal time period in rats have evaluated both emotional behavior as well as cognitive/behavioral functioning. One of the first research groups to look at cannabinoid exposure found poorer performance on cognitive tasks, such as maze learning, in immature rats compared to mature rats treated with THC [76]. Schneider and Koch (2003, 2005) have reported alterations in pubertal rats treated with the receptor agonist WIN, discrepancies in performance range from sensorimotor functioning, object recognition memory, and social behavior [77, 78]. A more recent study by Schneider and colleagues (2008) found that chronic WIN treated pubertal rats demonstrated object/social recognition deficits, which the authors suggest is consistent with impairment in short-term information processing. Particularly, immature animals demonstrated more pronounced behavioral alterations as compared to mature animals after acute exposure to WIN, and more lasting deficits in social play and grooming behaviors [79]. Deficits in object recognition have also been reported in male and female pubertal rats treated with a cannabinoid receptor agonist as well as THC [80�], and there is some support that findings are consistent across age groups.

Spatial functioning in adolescent rats has also shown affected by acute THC treatment [83]. In a recent investigation by Abush and colleagues (2012), chronic WIN treatment was found to result in both acute and longer term effects not only in spatial memory and object recognition, but interestingly, long term potentiation in areas such as nucleus accumbens pathways [84]. Studies are actively evaluating emotional functioning and neurochemical transmission in adolescent animals after exposure to cannabinoid agonists, as well as how cannabinoids moderate state-dependent learning based on brain regions [85, 86]. While this is not an exhaustive review of the preclinical findings, in general, the data suggest differential and often negative impact on adolescent animals compared to adult animals exposed to THC or other cannabinoid agonists in behavioral, emotional, and social outcomes. The animal work is particularly important to highlight, given the consistency in many adolescent neurocognitive and neuroimaging studies conducted with human subjects reporting regular use of marijuana, as the findings often point to the deleterious effects on brain functioning compared to non-using controls.


Understanding Memory in Advertising

Advertisers and those who measure the impact of advertising are obsessed with memory. If advertising is to be successful, it has to stick in the consumer’s memory—or so the saying goes. But what exactly is that thing called memory, how long does it linger, and how do we measure it?

At the first level, memory can be divided into two types: Explicit memory, which refers to information we are aware of (the facts and events we can consciously access), and implicit memory, which refers to information we’re not consciously aware of (it’s stored in our brain and can affect behavior, but we can’t recall it). Explicit memory can be further divided into episodic memory and semantic memory. Episodic memory is the memory of an event in space and time—it includes other contextual information present at that time. On the other hand, semantic memory is a more structured record of facts, meanings, concepts and knowledge that is divorced from accompanying episodic details.

How do these various types of memories come in play in advertising? Advertising memories that we retrieve through standard recall and recognition cues are episodic. Here are a few questions that researchers might use to retrieve those memories: What brand of smartphone did you see advertised on TV last night? Do you recall if it was a Samsung Galaxy S7 or an iPhone 7? What if I told you it aired during last night’s episode of Madam Secretary? What if I told you it featured a father shooting a video of his young daughter playing a scene in Romeo and Juliet? But very often, consumers cannot tell us exactly how they came to know what they know about a brand. They know that Coca-Cola is refreshing, for instance, but cannot tell us exactly how they first came by that information. Was it an ad they saw, a word from a friend, a personal experience? That memory is semantic. Unconscious associations (such as a non-accessible childhood experience of drinking Coca-Cola during a hot summer) create implicit memories that can continue to affect brand preferences much later in life.

Memory is a complex concept, with different types of memories serving different roles, and the nature and content of our memories changes over time. If consumers can’t remember what they saw last night without a prompt, but something they saw years ago still has an effect on them, it’s important that we, as researchers, gain a better understanding of the impact that time has on memory.

Research tells us that memories start to decay immediately after they’re formed. That decay follows a curve that is very steep at the beginning (the steepest rate of decay occurs in the first 24 hours) and levels off over time. In a controlled experiment, Nielsen tested the memorability of 49 video ads immediately after consumers were exposed to them in a clutter reel, and we tested that memorability again the day after exposure (among a separate group of people). Levels of branded recognition had fallen nearly in half overnight. This isn’t just happening in the lab: Nielsen’s in-market tracking data shows similar patterns.

Does this rapid decay of memory spell doom for the ad industry? Not at all. The fact that a specific memory can’t be recalled doesn’t mean that it’s fully gone. For one, relearning explicit information that is almost fully forgotten is much faster than learning it the first time around. Practice (repetition) indeed makes perfect—and can help create durable memories. In addition, the most striking revelation of a decay curve is not the steep decline at the start, but rather the leveling-off that occurs over the long term. We studied brand memorability decay over a longer period of time for a number of digital video ads recently, and while recall dropped for all ads by 50% in the first 24 hours (as was the case in our earlier study), it still stood at that same 50% level five days later for half of the brands.

What does this tell us about measuring memory? First, that time between exposure and measurement matters. The 24 hours mark is ideal because that’s the point where the memory curve starts to flatten. Second, that advertising memories are encoded in context (asking questions about the show in which the ad aired, for instance, is going to help consumers remember that ad). Finally, that memories can endure—either via repetition for explicit types of memories, or via implicit internalization.

To help advertisers in today’s cluttered advertising environment, researchers need to measure memory in all its forms. At Nielsen, we capture important performance metrics for ad memorability using carefully crafted surveys, and those surveys are conducted in a way that produces reliable benchmarks for the industry. And with the tools of neuroscience*, we can now measure brain activity during exposure and monitor both explicit and implicit memory systems with second-by-second granularity. Together, these different research techniques are helping us better understand the nature of memories—and how memories and advertising come to interact.

*See From theory to common practice: consumer neuroscience goes mainstream in VOL 1 ISSUE 2 of the Nielsen Journal of Measurement.


Conclusions

Our review indicates that approximately 1 in 4 CYP are demonstrating problematic smartphone use, a pattern of behaviour that mirrors that of a behavioural addiction. A consistent relationship has been demonstrated between PSU and deleterious mental health symptoms including: depression anxiety high levels of perceived stress and poor sleep. Younger populations are more vulnerable to psychopathological developments, and harmful behaviours and mental health conditions established in childhood can shape the subsequent life course. Further work is urgently needed to develop assessment tools for PSU, and prevent possible long-term widespread harmful impact on this and future generations’ mental health and wellbeing.


Media Multitasking Disrupts Memory, Even in Young Adults

The bulky, modern human brain evolved hundreds of thousands of years ago and, for the most part, has remained largely unchanged. That is, it is innately tuned to analog information&mdashto focus on the hunt at hand or perhaps the forage for wild plants. Yet we now pummel our ancient thinking organ with a daily deluge of digital information that many scientists believe may have enduring and worrisome effects.

A new study published today in Nature supports the concern. The research suggests that &ldquomedia multitasking&rdquo&mdashor engaging with multiple forms of digital or screen-based media simultaneously, whether they are television, texting or Instagram&mdashmay impair attention in young adults, worsening their ability to later recall specific situations or experiences.

The authors of the new paper used electroencephalography&mdasha technique that measures brain activity&mdashand eye tracking to assess attention in 80 young adults between the ages of 18 and 26. The study participants were first presented with images of objects on a computer screen and asked to classify the pleasantness or size of each one. After a 10-minute break, the subjects were then shown additional objects and asked whether they were already classified or new. By analyzing these individuals&rsquo brain and eye responses as they were tasked with remembering, the researchers could identify the number of lapses in their attention. These findings were then compared to the results of a questionnaire the participants were asked to fill out that quantified everyday attention, mind wandering and media multitasking.

Higher reported media multitasking correlated with a tendency toward attentional lapses and decreased pupil diameter, a known marker of reduced attention. And attention gaps just prior to remembering were linked with forgetting the earlier images and reduced brain-signal patterns known to be associated with episodic memory&mdashthe recall of particular events.

Previous work had shown a connection between media multitasking and poorer episodic memory. The new findings offer clues as to why this might be the case. &ldquoWe found evidence that one&rsquos ability to sustain attention helps to explain the relationship between heavier media multitasking and worse memory,&rdquo says the paper&rsquos lead author Kevin Madore, a postdoctoral fellow in the department of psychology at Stanford University. &ldquoIndividuals who are heavier media multitaskers may also show worse memory because they have lower sustained attention ability.&rdquo

&ldquoThis is an impressive study,&rdquo comments Daphne Bavelier, a professor of psychology at the University of Geneva in Switzerland, who was not involved in the new research. &ldquoThe work is important as it identifies a source of interindividual variability when one is cued to remember information&rdquo&mdashthe differences in attention among the study participants. &ldquoThese findings are novel and tell us something important about the relationship between attention and memory, and their link to everyday behavior . [something] we did not know before,&rdquo adds Harvard University psychologist Daniel L. Schacter, who was also not involved in the study.

Madore points out that the new findings are, for now, correlational. They do not indicate if media multitasking leads to impaired attention or if people with worse attention and memory are just more prone to digital distractions. They also do not necessarily implicate any specific media source as detrimental to the brain. As work by Bavelier found, action video games in particular harbor plenty of potential for improving brain function.

But Madore and his colleagues, including senior author of the paper and Stanford psychologist Anthony D. Wagner, hope to clarify these unknowns in future studies. They also hope to pursue attention-training interventions that could help improve attention and memory in people prone to distraction.

With winter looming and the COVID-19 pandemic keeping us indoors, Madore feels the new study stresses the need to be mindful of how we engage with media. &ldquoI think our data point to the importance of being consciously aware of attentiveness,&rdquo he says, whether that awareness means resisting media multitasking during school lectures or work Zoom sessions or making sure not to idly flip through your Facebook feed while half watching the new Borat movie.

ABOUT THE AUTHOR(S)

Bret Stetka is a writer based in New York City and editorial director of Medscape Neurology (a subsidiary of WebMD). His work has appeared in Wired, NPR and the Atlantic. He graduated from the University of Virginia School of Medicine in 2005.


The health effects of screen time on children: A research roundup

This research roundup looks at the effects of screen time on children’s health.

This research roundup, originally published in May 2019, has been updated to include a recent systematic review and meta-analysis looking at the effects of screen time on academic performance.

Gone are visions of idyllic childhoods spent frolicking in fields and playing in pastures for many kids, green grass has been replaced with smartphone screens.

This is in spite of official guidelines from the American Academy of Pediatrics, which recommends less than one hour per day of screen time for children between the ages of 2 and 5, and, for older children, “consistent limits” on screen time and prioritization of sleep, physical activity and other healthy behaviors over media use. Just last month the World Health Organization issued guidelines on the subject, stressing that children between the ages of 2 and 4 should have no more than one hour of screen time per day.

The ubiquity of screens and their prominence in everyday life has drawn criticism and concerns, with Microsoft veteran and philanthropist Melinda Gates writing about not being “prepared for smartphones and social media” as a parent and news headlines questioning whether smartphones have “destroyed a generation.”

But what does the research say? This roundup looks at the effects of screen time on children’s health. Studies range from childhood to adolescence and focus on topics including sleep, developmental progress, depression and successful interventions to reduce screen time.

This study analyzes parent-reported data about screen time and behavioral issues such as inattention and aggressiveness for a sample of 2,322 Canadian preschool-age children. Researchers found that over 13% of kids in the sample were exposed to over two hours of screen time each day including watching TV and DVDs, playing video games or using a computer, tablet or mobile device. The effects: Kids who were exposed to more screen time “showed significantly increased behavior problems at five-years,” the authors write. “Briefly, children who watched more than 2 hours of screen time/day had increased externalizing [e.g., attention and behavior], internalizing [e.g., anxiety and depression], and total behavior problems scores compared to children who watched less than 20 minutes.” Attention problems in particular were apparent in children who had over two hours of screen time each day.

Toddlers who use mobile devices daily are more likely to experience speech delays, according to an analysis of parent-reported data on 893 children in the greater Toronto area of Canada. While 78% of parents said their kids spent no time on mobile devices, the other 22% reported a range of 1.4 to 300 minutes daily, with a median of 15.7 minutes.

In total, 6.6% of parents reported expressive speech delays (i.e., late to begin talking). The prevalence of other communication delays, such as lack of use of gestures and eye gaze, was 8.8%. The researchers found a positive association between mobile device use and expressive speech delays. “An increase in 30 minutes per day in mobile media device use was associated with a 2.3 times increased risk of parent-reported expressive speech delay,” the authors write. Other communication delays were not linked to device use. The researchers suggest the connection between device use and expressive speech delays might be explained by the fact that past research has shown infants “have difficulty applying what they learn across different contexts.” An alternate explanation is that these children who spend more time with devices might have less exposure to speech from caregivers.

Is screen time detrimental to child development? This study looks at data collected from 2,441 mothers and children in Canada at three different time points – when the children were 2, 3 and 5 years old. The researchers were interested in the total number of hours the children spent looking at screens each week as well as their progress in various developmental areas such as fine motor skills, communication and problem solving. The average amount of screen time for the age groups in the study: 17, 25 and 11 hours of television per week for 2-, 3-, and 5-year olds, respectively.

The researchers found that kids who spent more time watching screens at ages 2 and 3 did worse on developmental tests at the subsequent time points of 3 and 5 years. “To our knowledge, the present study is the first to provide evidence of a directional association between screen time and poor performance on development screening tests among very young children,” the authors write.

The researchers suggest that excessive screen time leads to developmental delays, rather than the other way around – negating the notion that children with developmental delays might receive more screen time to manage their behavior.

The three phase data capture supports this explanation because children with greater screen time at one time point go on at the next time point to have poorer developmental progress, but children with poor developmental performance at an earlier time point do not receive increased screen time at later time points.

This publication consists of both a systematic review and meta-analysis of research on the relationship between screen time and academic performance. The authors identified 58 studies to include in the systematic review, which provides a summary of the qualitative effects of screen time 30 of these studies were included in the subsequent meta-analysis, which the authors used to calculate the effect size of screen time on academic performance.

The 58 studies in the systematic review included 480,479 participants ranging from four to 18 years of age. The articles were published between 1958 and 2018 and represent the efforts of researchers around the world. The studies looked at computer, internet, mobile phone, television and video game use individually, as well as overall screen time. Outcomes of interest included school grades, performance on academic achievement tests, academic failure data, or self-reported academic achievement or school performance.

The key finding from the systematic review was that in most of the papers reviewed, as time spent watching television increased, academic performance suffered. Relationships were less clear-cut for other types of screen use.

The meta-analysis, which focused on a subset of 106,653 participants from the larger sample, did not find an association between overall screen time and academic performance. When the authors analyzed the data by type of activity, they found television watching was linked to poorer overall academic performance as well as poorer language and mathematics performance, separately. Time spent playing video games was negatively linked with composite academic performance scores, too. Analyzing the data further by age, the authors found that time spent with screens had a larger negative association with academic performance for adolescents than children.

“The findings from this systematic review and meta-analysis suggest that each screen-based activity should be analyzed individually because of its specific association with academic performance,” the authors conclude. “This study highlights the need for further research into the association of internet, computer, and mobile phone use with academic performance in children and adolescents. These associations seem to be complex and may be moderated and/or mediated by potential factors, such as purpose, content, and context of screen media use.”

The authors suggest that educators and health professionals should focus screen time reduction efforts on television and video games for their negative connections to academic performance and potential health risks due to their sedentary nature.

Screen Time Is Associated with Adiposity and Insulin Resistance in Children
Nightingale, Claire M. et al. Archives of Disease in Childhood, July 2017.

This study looks at the relationship between screen time and Type 2 diabetes risk factors, like being severely overweight, among 4,495 schoolchildren in the United Kingdom between the ages of 9 and 10. The short of it: Kids who spent over three hours daily on screen time were less lean and more likely to show signs of insulin resistance, which can contribute to the development of Type 2 diabetes, compared with their peers who reported one hour or less of screen time each day. Black children were more likely to spend over three hours daily on devices compared with their white and south Asian peers – 23% of black children fell into that group, compared with 16% of white children and 16% of south Asian children.

Digital Media and Sleep in Childhood and Adolescence
LeBourgeois, Monique K. et al. Pediatrics, November 2017.

This report summarizes 67 studies looking at associations between screen time and sleep health – adequate sleep length and quality — in children and adolescents. The main takeaways: A majority (90%) of the studies included in a systematic review of research on screen time in children and teenagers found adverse associations between screen time and sleep health – primarily because of later bedtimes and less time spent sleeping. Delving deeper, underlying mechanisms include “time displacement” (think scrolling Instagram for an hour that might otherwise be spent sleeping), psychological stimulation from content consumed and impacts of screen light on sleep patterns. The upshot? These kids are tired. The previously cited research review also indicates that a majority of studies saw a relationship between tiredness and screen time.

This study analyzes data from the 2011, 2013, 2015 and 2017 cycles of a nationally-administered, school-based survey on various health-related behaviors related to the leading causes of death and disability in the U.S. The researchers were interested in whether respondents met the recommendations for time spent on sleep, physical activity and screen time in a given day. A total of 59,397 adolescents were included in the data set.

The findings indicate that only 5% of adolescents surveyed met all three guidelines – that is, getting the recommended amount of sleep and physical activity and limiting screen time to less than two hours per day. There were disparities among the sample in terms of the odds of meeting all of the recommendations: 16- and 17-year-olds were less likely than those aged 14 and younger to meet all the guidelines black, Hispanic/Latino and Asian participants were less likely to meet the three guidelines than white participants overweight and obese participants were less likely to meet the guidelines than normal weight participants participants who reported marijuana use were less likely to meet the guidelines than those who did not. Participants who reported depressive symptoms were also less likely to meet all the guidelines.

This study looks at the same three outcomes examined above, but adds another component – “global cognition.” This is an overall cognition score assessed by the National Institutes of Health Toolbox – an iPad-based neuro-behavioral screening tool. The assessment measures various cognitive functions including memory, attention, vocabulary and processing speed. The sample included 4,520 participants between the ages of 8 and 11. Only 5% of participants met all three recommendations – and they were the better for it. “Compared with meeting none of the recommendations, associations with superior global cognition were found in participants who met all three recommendations, the screen time recommendation only, and both the screen time and the sleep recommendations,” the authors write.

This study looks at the relationship between screen time and depression and suicide rates in 506,820 adolescents in the U.S. between 2010 and 2015. The data on screen time use and mental health issues came from two nationally representative surveys of students in grades 8 through 12. Suicide rates were calculated from national statistics collected by the Centers for Disease Control and Prevention’s Fatal Injury Reports.

The analysis finds a “clear pattern linking screen activities with higher levels of depressive symptoms/suicide-related outcomes [suicidal ideation — that is, thinking about suicide — and attempts] and nonscreen activities with lower levels.” Among participants who used devices for over five hours each day, nearly half – 48% — reported at least one suicide-related outcome. In comparison, 29% of those who used devices for just an hour per day had at least one suicide-related outcome.

Overall, during the time studied, suicide rates, depressive symptoms and suicide-related outcomes increased. Girls accounted for most of the rise – they were more likely to experience depressive symptoms and suicide-related outcomes than boys they also experienced stronger effects of screen time on mental health. In particular, girls, but not boys, had a significant correlation between social media use and depressive symptoms.

This review looks at 10 systematic reviews and meta-analyses of research on interventions to reduce sedentary behaviors such as screen time among children and adolescents. The authors found that all of the included reviews determined “some level of effectiveness in reducing time spent in sedentary behavior.” Effects, however, were small. Interventions tended to be more successful among children younger than 6 years old. Strategies that were effective included restricting access to television through TV monitors, systems that use TV as a reward for physical activity and behavioral interventions such as setting goals and developing schedules for screen time.

For more research on the effects of screen time, check out our write ups of research that shows how smartphones make people unhappy and how they’re distracting even when they aren’t in use.


What your choice of smartphone says about you

Choice of smartphone provides valuable information about its owner.

This is one of the findings of a doctoral study conducted by Heather Shaw, from University of Lincoln's School of Psychology. She is presenting her work today, Thursday 1 September, to the British Psychological Society Social Psychology Section annual conference in Cardiff.

Miss Shaw and her fellow researchers conducted two studies of personality differences between iPhone and Android smartphone users. Lancaster University was also involved in the study.

In the first study the researchers asked 240 participants to complete a questionnaire about characteristics they associate with users of each smartphone brand.

In the second study they tested these stereotypes against actual personality traits of 530 Android and iPhone smartphone users.

The results from the first study showed that Android users are perceived to have greater levels of honesty and humility, agreeableness and openness personality traits but are seen as less extroverted than iPhone users.

The results from the second study showed that most of the personality stereotypes did not occur in reality, as only honesty and humility was found in greater amounts within Android users.

However, they did find that women were twice more likely to own an iPhone than an Android Phone. When measuring the characteristic 'avoidance of similarity' which describes whether people like having the same products as others, Android Users avoided similarity more than iPhone users. Finally, iPhone users thought it was more important to have a high status phone than Android users.

Heather explained "This study provides new insights into personality differences between different types of smartphone users. Smartphone choice is the most basic level of smartphone personalisation, and even this can tell us a lot about the user."

"Imagine if we further researched how personality traits relate to the applications people download. It is becoming more and more apparent that smartphones are becoming a mini digital version of the user, and many of us don't like it when other people use our phones because it can reveal so much about us."